




"""simple docstring"""





import glob
import os
import random
from string import ascii_lowercase, digits

import cva
A				=							""""""
A				=							""""""
A				=							""""""
A				=							1  # (0 is vertical, 1 is horizontal)

def        _UpperCamelCase     (      )      ->      None:


			"""simple docstring"""
			__UpperCAmelCase						,  __UpperCAmelCase						:			Optional[Any]												=       get_dataset(UpperCamelCase					,							UpperCamelCase				)
			print("Processing..."				)
			__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						:			List[Any]												=       update_image_and_anno(UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)

			for index, image in enumerate(UpperCamelCase				):
						# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
						__UpperCAmelCase						:			List[Any]												=       random_chars(32				)
						__UpperCAmelCase						:			List[str]												=       paths[index].split(os.sep				)[-1].rsplit("."					,							1				)[0]
						__UpperCAmelCase						:			str												=       f"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
						cva.imwrite(f"/{file_root}.jpg"					,							UpperCamelCase					,							[cva.IMWRITE_JPEG_QUALITY, 85]				)
						print(f"Success {index+1}/{len(UpperCamelCase				)} with {file_name}"				)
						__UpperCAmelCase						:			Dict												=       []
						for anno in new_annos[index]:
									__UpperCAmelCase						:			Any												=       f"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
									annos_list.append(UpperCamelCase				)
						with open(f"/{file_root}.txt"					,							"w"				) as outfile:
									outfile.write("\n".join(line for line in annos_list				)				)

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      tuple[list, list]:


			"""simple docstring"""
			__UpperCAmelCase						:			Optional[Any]												=       []
			__UpperCAmelCase						:			Optional[int]												=       []
			for label_file in glob.glob(os.path.join(UpperCamelCase					,							"*.txt"				)				):
						__UpperCAmelCase						:			str												=       label_file.split(os.sep				)[-1].rsplit("."					,							1				)[0]
						with open(UpperCamelCase				) as in_file:
									__UpperCAmelCase						:			int												=       in_file.readlines()
						__UpperCAmelCase						:			Dict												=       os.path.join(UpperCamelCase					,							f"{label_name}.jpg"				)

						__UpperCAmelCase						:			int												=       []
						for obj_list in obj_lists:
									__UpperCAmelCase						:			Union[str, Any]												=       obj_list.rstrip("\n"				).split(" "				)
									boxes.append(
									    [
									        int(obj[0]				),
									        float(obj[1]				),
									        float(obj[2]				),
									        float(obj[3]				),
									        float(obj[4]				),
									    ]				)
						if not boxes:
									continue
						img_paths.append(UpperCamelCase				)
						labels.append(UpperCamelCase				)
			return img_paths, labels

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase = 1				)      ->      tuple[list, list, list]:


			"""simple docstring"""
			__UpperCAmelCase						:			Dict												=       []
			__UpperCAmelCase						:			Tuple												=       []
			__UpperCAmelCase						:			Optional[Any]												=       []
			for idx in range(len(UpperCamelCase				)				):
						__UpperCAmelCase						:			Tuple												=       []
						__UpperCAmelCase						:			Optional[Any]												=       img_list[idx]
						path_list.append(UpperCamelCase				)
						__UpperCAmelCase						:			Union[str, Any]												=       anno_list[idx]
						__UpperCAmelCase						:			List[str]												=       cva.imread(UpperCamelCase				)
						if flip_type == 1:
									__UpperCAmelCase						:			List[str]												=       cva.flip(UpperCamelCase					,							UpperCamelCase				)
									for bbox in img_annos:
												__UpperCAmelCase						:			int												=       1 - bbox[1]
												new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]				)
						elif flip_type == 0:
									__UpperCAmelCase						:			List[str]												=       cva.flip(UpperCamelCase					,							UpperCamelCase				)
									for bbox in img_annos:
												__UpperCAmelCase						:			Optional[int]												=       1 - bbox[2]
												new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]				)
						new_annos_lists.append(UpperCamelCase				)
						new_imgs_list.append(UpperCamelCase				)
			return new_imgs_list, new_annos_lists, path_list

def        _UpperCamelCase     (      UpperCamelCase = 32				)      ->      str:


			"""simple docstring"""
			assert number_char > 1, "The number of character should greater than 1"
			__UpperCAmelCase						:			List[str]												=       ascii_lowercase + digits
			return "".join(random.choice(UpperCamelCase				) for _ in range(UpperCamelCase				)				)


if __name__ == "__main__":
					main()
					print("""DONE ✅""")



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					['''LongformerTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''LongformerForMaskedLM''',
       '''LongformerForMultipleChoice''',
       '''LongformerForQuestionAnswering''',
       '''LongformerForSequenceClassification''',
       '''LongformerForTokenClassification''',
       '''LongformerModel''',
       '''LongformerPreTrainedModel''',
       '''LongformerSelfAttention''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFLongformerForMaskedLM''',
       '''TFLongformerForMultipleChoice''',
       '''TFLongformerForQuestionAnswering''',
       '''TFLongformerForSequenceClassification''',
       '''TFLongformerForTokenClassification''',
       '''TFLongformerModel''',
       '''TFLongformerPreTrainedModel''',
       '''TFLongformerSelfAttention''',
   ]


if TYPE_CHECKING:
   from .configuration_longformer import (
       LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       LongformerConfig,
       LongformerOnnxConfig,
   )
   from .tokenization_longformer import LongformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_longformer_fast import LongformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_longformer import (
          LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          LongformerForMaskedLM,
          LongformerForMultipleChoice,
          LongformerForQuestionAnswering,
          LongformerForSequenceClassification,
          LongformerForTokenClassification,
          LongformerModel,
          LongformerPreTrainedModel,
          LongformerSelfAttention,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_longformer import (
          TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLongformerForMaskedLM,
          TFLongformerForMultipleChoice,
          TFLongformerForQuestionAnswering,
          TFLongformerForSequenceClassification,
          TFLongformerForTokenClassification,
          TFLongformerModel,
          TFLongformerPreTrainedModel,
          TFLongformerSelfAttention,
      )

else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




